import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly
import plotly.express as px

Goal: Evaluate China’s values regarding family from 1990-2018 using the World Values Survey data.

Context: Family Values in the People's Republic of China

Confucian Filial Piety: The Confucian virtue of filial piety (孝xiao) stresses respect for parents, elders and ancestors, as well as emphasized patriarchal family structures where wives served their husbands and mothers served their sons. Confucianism conceptualized the family as analogous to the human body or a living tree, where every role in the family was a distinct component of an inseparable entity. A filial son is obedient and loyal; a father is strict and benevolent.

Modernization Theory: Debunked in the 1970s. As a society becomes more industrialized and modern, the Modernization Theory argues that the role of the individual increases in importance and the family decreases in importance.

Importing Data -- Six Datasets from the World Values Survey

Each survey has approximately 1000 respondents with 400+ Questions.

df1990 = pd.read_csv('Box Sync/Forge - Node/World Values Survey/1990.csv')
df1995 = pd.read_csv('Box Sync/Forge - Node/World Values Survey/1995.csv')
df2001 = pd.read_csv('Box Sync/Forge - Node/World Values Survey/2001.csv')
df2007 = pd.read_csv('Box Sync/Forge - Node/World Values Survey/2007.csv')
df2012 = pd.read_csv('Box Sync/Forge - Node/World Values Survey/2012.csv')
df2018 = pd.read_csv('Box Sync/Forge - Node/World Values Survey/2018.csv')
pd.set_option('max_columns', None) 

df1990

	V1: WVS wave	V2: Country/region	V2A: Country/region [with split ups]	V3: Original respondent number	V4: Important in life: Work	V5: Important in life: Family	V6: Important in life: Friends	V7: Important in life: Leisure time	V8: Important in life: Politics	V9: Important in life: Religion	V10: How often discusses political matters with friends	V11: Persuading friends, relatives or fellow workers	V12: Would give part of my income for the environment	V13: Increase in taxes if used to prevent environmental pollution	V14: Government should reduce environmental pollution	V15: All talk about the environment make people anxious	V16: Combatting unemployment, we have to accept environmental problems	V17: Protecting environment and fighting pollution is less urgent than suggested	V18: Feeling of happiness	V19: Member: Belong to social welfare service for elderly	V20: Member: Belong to religious organization	V21: Member: Belong to education, arts, music or cultural activities	V22: Member: Belong to labour unions	V23: Member: Belong to political parties	V24: Member: Belong to local political actions	V25: Member: Belong to human rights	V26: Member: Belong to conservation, the environment, ecology	V27: Member: Belong to professional associations	V28: Member: Belong to youth work	V29: Member: Belong to sports or recreation	V30: Member: Belong to women´s group	V31: Member: Belong to peace movement	V32: Member: Belong to animal rights	V33: Member: Belong to organization concerned with health	V34: Member: Belong to other groups	V35: Member: Belong to none	V36: Member: Don´t know	V37: Voluntary work: Unpaid work social welfare service for elderly, handicapped or deprived people	V38: Voluntary work: Unpaid work religious or church organization	V39: Voluntary work: Unpaid work education, arts, music or cultural activities	V40: Voluntary work: Unpaid work labour unions	V41: Voluntary work: Unpaid work political parties or groups	V42: Voluntary work: Unpaid work local political action groups	V43: Voluntary work: Unpaid work human rights	V44: Voluntary work: Unpaid work environment, conservation, ecology	V45: Voluntary work: Unpaid work professional associations	V46: Voluntary work: Unpaid work youth work	V47: Voluntary work: Unpaid work sports or recreation	V48: Voluntary work: Unpaid work women´s group	V49: Voluntary work: Unpaid work peace movement	V50: Voluntary work: Unpaid work animal rights	V51: Voluntary work: Unpaid work organization concerned with health	V52: Voluntary work: Unpaid work other groups	V53: Voluntary work: Unpaid work none	V54: Voluntary work: Don´t know	V55: Reasons voluntary work: Solidarity with the poor and disadvantaged	V56: Reasons voluntary work: Compassion for those in need	V57: Reasons voluntary work: Opportunity to repay something	V58: Reasons voluntary work: Sense of duty, moral, obligation	V59: Reasons voluntary work: Identifying with people who suffer	V60: Reasons voluntary work: Time on my hands	V61: Reasons voluntary work: Personal satisfaction	V62: Reasons voluntary work: Religious belief	V63: Reasons voluntary work: Help disadvantaged people	V64: Reasons voluntary work: Make a contribution to my local community	V65: Reasons voluntary work: Bring about social or political change	V66: Reasons voluntary work: For social reasons	V67: Reasons voluntary work: Gain new skills and useful experience	V68: Reasons voluntary work: Did not want to, but could not refuse	V69: Neighbours: People with a criminal record	V70: Neighbours: People of a different race	V71: Neighbours: Left wing extremists	V72: Neighbours: Heavy drinkers	V73: Neighbours: Right wing extremists	V74: Neighbours: People with large families	V75: Neighbours: Emotionally unstable people	V76: Neighbours: Muslims	V77: Neighbours: Immigrants/foreign workers	V78: Neighbours: People who have AIDS	V79: Neighbours: Drug addicts	V80: Neighbours: Homosexuals	V81: Neighbours: Jews	V82: Neighbours: Hindus	V82A: Neighbours: Political Extremists	V83: State of health (subjective)	V84: Ever felt very excited or interested	V85: Ever felt restless	V86: Ever felt proud because someone complimented you	V87: Ever felt very lonely or remote from other people	V88: Ever felt pleased about having accomplished something	V89: Ever felt bored	V90: Ever felt on top of the world	V91: Ever felt depressed or very unhappy	V92: Ever felt that things were going your way	V93: Ever felt upset because somebody criticized you	V94: Most people can be trusted	V95: How much freedom of choice and control	V96: Satisfaction with your life	V97: Why are there people living in need: first	V98: Why are there people living in need: second	V99: Important in a job: good pay	V100: Important in a job: pleasant people to work with	V101: Important in a job: not too much pressure	V102: Important in a job: good job security	V103: Important in a job: good chances for promotion	V104: Important in a job: a respected job	V105: Important in a job: good hours	V106: Important in a job: an opportunity to use initiative	V107: Important in a job: a useful job for society	V108: Important in a job: generous holidays	V109: Important in a job: meeting people	V110: Important in a job: that you can achieve something	V111: Important in a job: a responsible job	V112: Important in a job: a job that is interesting	V113: Important in a job: a job that meets one´s abilities	V114: Important in a job: none of these	V115: Degree of pride in your work	V116: Job satisfaction	V117: Freedom decision taking in job	V118: Why people work: work is like a business transaction	V119: Why people work: I do the best I can regardless of pay	V120: Why people work: I wouldn’t work if I didn’t have to	V121: Why people work: I wouldn´t work if work interfered my life	V122: Why people work: work most important in my life	V123: Why people work: I never had a paid job	V124: Why people work: don’t know	V125: Fairness: One secretary is paid more	V126: How business and industry should be managed	V127: Following instructions at work	V128: Jobs scarce: Men should have more right to a job than women	V129: Jobs scarce: older people should be forced to retire	V130: Jobs scarce: Employers should give priority to (nation) people than immigrants	V131: Unfair to give work to handicapped people when able bodied people can´t find jobs	V132: Satisfaction with financial situation of household	V133: Thinking about meaning and purpose of life	V134: Thinking about death	V135: Life is meaningful because God exits	V136: Try to get the best out of life	V137: Death is inevitable	V138: Death has meaning if you believe in God	V139: Death is a natural resting point	V140: Sorrow has meaning if you believe in God	V141: Life has no meaning	V142: Statement: good and evil	V143: Belong to religious denomination	V144: Religious denomination	V145: Which former religious denomination	V146: Raised religiously	V147: How often do you attend religious services	V148: Important: Religious service birth	V149: Important: Religious service marriage	V150: Important: Religious service death	V151: Religious person	V152: Churches give answers: moral problems	V153: Churches give answers: the problems of family life	V154: Churches give answers: people´s spiritual needs	V155: Churches give answers: the social problems	V156: Churches speak out on: disarmament	V157: Churches speak out on: abortion	V158: Churches speak out on: third world problems	V159: Churches speak out on: extramarital affairs	V160: Churches speak out on: unemployment	V161: Churches speak out on: racial discrimination	V162: Churches speak out on: euthanasia	V163: Churches speak out on: homosexuality	V164: Churches speak out on: ecology and environmental issues	V165: Churches speak out on: government policy	V166: Believe in: God	V167: Believe in: life after death	V168: Believe in: people have a soul	V169: Believe in: devil	V170: Believe in: hell	V171: Believe in: heaven	V172: Believe in: sin	V173: Believe in: resurrection of the dead	V174: Believe in: re-incarnation	V175: Personal God vs. Spirit or Life Force	V176: How important is God in your life	V177: Get comfort and strength from religion	V178: Moments of prayer, meditation...	V179: Pray to God outside of religious services (ii)	V180: Satisfaction with home life	V181: Marital status	V182: Have you been married before	V183: Sharing with partner: attitudes towards religion	V184: Sharing with partner: moral standards	V185: Sharing with partner: social attitudes	V186: Sharing with partner: political views	V187: Sharing with partner: sexual attitudes	V188: Sharing with partner: no sharing attitudes	V189: Sharing with partner: don´t know or missing	V190: Sharing with parents: attitudes towards religion	V191: Sharing with parents: moral standards´	V192: Sharing with parents: social attitudes	V193: Sharing with parents: political views	V194: Sharing with parents: sexual attitudes	V195: Sharing with parents: no sharing attitudes	V196: Sharing with parents: don´t know or missing	V197: Enjoy sexual freedom	V198: Important for succesful marriage: Faithfulness	V199: Important for succesful marriage: Adequate income	V200: Important for succesful marriage: Same social background	V201: Important for succesful marriage: Respect and appreciation	V202: Important for succesful marriage: Religious beliefs	V203: Important for succesful marriage: Good housing	V204: Important for succesful marriage: Agreement on politics	V205: Important in succesful marriage: Understanding and tolerance	V206: Important for succesful marriage: Apart from in-laws	V207: Important for succesful marriage: Happy sexual relationship	V208: Important for succesful marriage: Sharing household chores	V209: Important for succesful marriage: Children	V210: Important for succesful marriage: Tastes and interests in common	V211: How many children do you have	V212: How many are still living at home	V213: Ideal number of children	V214: Child needs a home with father and mother	V215: A woman has to have children to be fulfilled	V216: Marriage is an out-dated institution	V217: Woman as a single parent	V218: Relationship working mother	V219: Pre-school child suffers with working mother	V220: Women want a home and children	V221: Being a housewife just as fulfilling	V222: Job best way for women to be independent	V223: Husband and wife should both contribute to income	V224: Respect and love for parents	V225: Parents responsibilities to their children	V226: Important child qualities: good manners	V227: Important child qualities: independence	V228: Important child qualities: hard work	V229: Important child qualities: feeling of responsibility	V230: Important child qualities: imagination	V231: Important child qualities: tolerance and respect for other people	V232: Important child qualities: thrift saving money and things	V233: Important child qualities: determination perseverance	V234: Important child qualities: religious faith	V235: Important child qualities: unselfishness	V236: Important child qualities: obedience	V237: Abortion when the mothers health is at risk	V238: Abortion when child physically handicapped	V239: Abortion when woman not married	V240: Abortion if not wanting more children	V241: Interest in politics	V242: Political action: signing a petition	V243: Political action: joining in boycotts	V244: Political action: attending lawful/peaceful demonstrations	V245: Political action: joining unofficial strikes	V246: Political action: occupying buildings or factories	V247: Freedom or equality	V248: Self positioning in political scale	V249: Basic kinds of attitudes concerning society	V250: Income equality	V251: Private vs state ownership of business	V252: Government responsibility	V253: Job taking of the unemployed	V254: Competition good or harmful	V255: Hard work brings success	V256: Wealth accumulation	V257: Aims of country: first choice	V258: Aims of country: second choice	V259: Aims of respondent: first choice	V260: Aims of respondent: second choice	V261: Most important: first choice	V262: Most important: second choice	V263: Willingness to fight for country	V264: Future changes: Less emphasis on money and material possessions	V265: Future changes: Less importance placed on work	V266: Future changes: More emphasis on technology	V267: Future changes: More emphasis on individual	V268: Future changes: Greater respect for authority	V269: Future changes: More emphasis on family life	V270: Future changes: A simple and more natural lifestyle	V271: Opinion about scientific advances	V272: Confidence: Churches	V273: Confidence: Armed Forces	V274: Confidence: Education System	V275: Confidence: Justice System	V276: Confidence: The Press	V277: Confidence: Labour Unions	V278: Confidence: The Police	V279: Confidence: Parliament	V280: Confidence: The Civil Services	V281: Confidence: Major Companies	V282: Confidence: Social Security System	V283: Confidence: Television	V283B: Confidence: The European Union	V284: Confidence: NATO	V285: Confidence: The Political Parties	V286: What thing are you proud of in your country -1st	V287: What thing are you proud of in your country -2nd	V288: Country is run by big interest vs. for all people’s benefit	V289: Confidence: The Government	V290: Approval: Ecology movement or nature protection	V291: Approval: Anti-nuclear energy movement	V292: Approval: Disarmament movement	V293: Approval: Human rights movement	V294: Approval: Women’s movement	V295: Approval: Anti-apartheid movement	V296: Justifiable: claiming government benefits	V297: Justifiable: avoiding a fare on public transport	V298: Justifiable: cheating on taxes	V299: Justifiable: buy stolen goods	V300: Justifiable: joyriding	V301: Justifiable: taking soft drugs	V302: Justifiable: keeping money that you have found	V303: Justifiable: lying	V304: Justifiable: adultery	V305: Justifiable: sex under the legal age of consent	V306: Justifiable: someone accepting a bribe	V307: Justifiable: homosexuality	V308: Justifiable: prostitution	V309: Justifiable: abortion	V310: Justifiable: divorce	V311: Justifiable: fighting with the police	V312: Justifiable: euthanasia	V313: Justifiable: suicide	V314: Justifiable: failing to report damage you’ve done accidentally to a parked vehicle	V315: Justifiable: threatening workers who refuse to join a strike	V316: Justifiable: killing in self-defence	V317: Justifiable: political assassination	V318: Justifiable: throwing away litter	V319: Justifiable: driving under influence of alcohol	V320: Geographical groups belonging to first	V321: Geographical groups belonging to second	V322: How proud of nationality	V323: Major changes in life	V324: New and old ideas	V325: Personal characteristics: Changes, worry or welcome possibility	V326: Personal characteristics: I usually count on being successful in everything I do	V327: Personal characteristics: I enjoy convincing others of my opinion	V328: Personal characteristics: I serve as a model for others	V329: Personal characteristics: I am good at getting what I want	V330: Personal characteristics: I own many things others envy me for	V331: Personal characteristics: I like to assume responsibility	V332: Personal characteristics: I am rarely unsure about how I should behave	V333: Personal characteristics: I often give others advice	V334: Personal characteristics: None of the above	V335: The economic system needs fundamental changes	V336: Our government should be made much more open to the public	V337: Allow more freedom for individuals	V338: I could do nothing about an unjust law	V339: Political reform is moving too rapidly	V340: How much do you trust your family	V341: Trust: Other people in country	V342: Trust: French	V344: Trust: Americans	V346: Trust: Russians	V347: Trust: Chinese	V347_01: Trust: Italians	V347_02: Trust: Latin Americans	V347_03: Trust: Japanese	V347_04: Trust: Blacks	V347_05: Trust: Germans	V347_06: Trust: Mapuche Indians	V347_07: Trust: Pascuences	V347_08: Trust: Argentines	V347_09: Trust: Peruvians	V347_10: Trust: Chinese Zhuan Nationality	V347_11: Trust: Chinese Hui Nationality	V347_12: Trust: English	V347_13: Trust: Slovaks	V347_14: Trust: Gypsies	V347_15: Trust: Poles	V347_16: Trust: Indian Hindus	V347_17: Trust: Indian Non-Hindus	V347_18: Trust: Pakistanis	V347_19: Trust: Nepalis	V347_20: Trust: Korean residents in Japan	V347_21: Trust: Chinese residents in Japan	V347_22: Trust: Mestizo	V347_23: Trust: Indians	V347_24: Trust: Central Americans	V347_25: Trust: Hausas	V347_26: Trust: Igbos	V347_27: Trust: Yorubas	V347_28: Trust: Ghanaians	V347_29: Trust: Czechs	V347_30: Trust: West Germans	V347_31: Trust: Your friends	V347_32: Trust: Your neighborhood	V347_33: Trust: White South Africans	V347_34: Trust: Black South Africans	V347_35: Trust: Coloured South Africans	V347_36: Trust: Asian South Africans	V347_37: Trust: Moroccans	V347_38: Trust: Portuguese	V347_39: Trust: Koreans	V347_40: Trust: Soviet Union people	V348: Born in this country: birth country	V349: When came to country	V350: Geographical groups belonging to least of all	V351: Which party would you vote for: first choice	V352: Which party would you vote for: second choice	V353: Sex	V354: Year of birth	V355: Age	V356: What age did you complete your education	V357: Do you live with your parents	V358: Employment status	V359: Profession/job	V360: Are you the chief wage earner in your house	V361: Is the chief wage earner employed now	V362: Chief wage earner profession/job	V363: Scale of incomes	V363CS: Income (country specific)	V364: Socio-economic status of respondent	V365: Time at the end of interview	V366: Total length of interview	V367: Respondent interested during the interview	V368: Size of town	V368CS: Size of town (country specific)	V369: Ethnic group	V370: Region where the interview was conducted	V372: Type of habitat	V373: Language in which interview was conducted	V375: Highest educational level attained	V376: Weight	V376A: Weight with split ups	V375CS: Education (country specific)	G001_CS: Geographical groups belonging to first (country specific)	G002_CS: Geographical groups belonging to second (country specific)	V377: Year survey	S024: Nation-Wave	S025: Nation Year	Y001: Materialist/postmaterialist 12-item index	Y002: Post-materialist index (4-item)	Y003: Autonomy Index	SACSECVAL: Overall Secular Values	SECVALWGT: Weight for overal secular values	RESEMAVAL: Emancipative values	RESEMAWEIGHT: Weight for Emancipative values	I_AUTHORITY: Overall Secular Values-1: Inverse respect for authority	I_NATIONALISM: Overall Secular Values-1: Inverse national pride	I_DEVOUT: Overall Secular Values-1: Inverse devoutness	DEFIANCE: Overall Secular Values-1: Defiance sub-index	WEIGHT1A: Overall Secular Values-1: Weight1a	I_RELIGIMP: Overall Secular Values-2: Inverse import of relig	I_RELIGBEL: Overall Secular Values-2: Inverse relig person	I_RELIGPRAC: Overall Secular Values-2: Inverse relig practice	DISBELIEF: Overall Secular Values-2: Disbelief sub-index	WEIGHT2A: Overall Secular Values-2: Weight2a	I_NORM1: Overall Secular Values-3: Inverse norm conform1	I_NORM2: Overall Secular Values-3: Inverse norm conform2	I_NORM3: Overall Secular Values-3: Inverse norm conform3	RELATIVISM: Overall Secular Values-3: Relativism	WEIGHT3A: Overall Secular Values-3: Weight	I_TRUSTARMY: Overall Secular Values-4: Inverse trust in army	I_TRUSTPOLICE: Overall Secular Values-4: Inverse trust in police	I_TRUSTCOURTS: Overall Secular Values-4: Inverse trust in courts	SCEPTICISM: Overall Secular Values-4: Scepticism index	WEIGHT4A: Overall Secular Values-4: Weight 4a	I_INDEP: Emancipative Values-1: Independence as kid quality	I_IMAGIN: Emancipative Values-1: Imagination as kid quality	I_NONOBED: Emancipative Values-1: Obedience not kid quality	AUTONOMY: Emancipative Values-1: Autonomy subindex	WEIGHT1B: Emancipative Values-1: Weight1b	I_WOMJOB: Emancipative Values-2: Gender equality: job	I_WOMPOL: Emancipative Values-2: Gender equality: politics	I_WOMEDU: Emancipative Values-2: Gender equality: education	EQUALITY: Emancipative Values-2: Equality sub-index	WEIGHT2B: Emancipative Values-2: Weight2b	I_HOMOLIB: Emancipative Values-3: Homosexuality acceptance	I_ABORTLIB: Emancipative Values-3: Abortion acceptable	I_DIVORLIB: Emancipative Values-3: Divorce acceptable	CHOICE: Emancipative Values-3: Choice sub-index	WEIGHT3B: Emancipative Values-3: Weight3b	I_VOICE1: Emancipative Values-4: Voice 1	I_VOICE2: Emancipative Values-4: Voice2	I_VOI2_00: Emancipative Values-4: Voi2_00 (auxiliary)	VOICE: Emancipative Values-4: Voice sub-index	WEIGHT4B: Emancipative Values-4: Weight 4b	S001: Study	S007: Unified respondent number	S018: Equilibrated weight-1000	S019: 1500 equilibrated weight	S021: Country - wave - study - set - year	COW: COW Country Code
0	2	156	156	1	1	2	2	3	4	4	2	1	2	1	3	3	3	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	1	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	1	2	-2	-2	-2	-2	-2	-2	-2	-2	-2	-2	-2	-2	-2	-2	2	2	-4	2	-4	2	2	1	2	1	1	1	-4	-4	-4	1	1	2	1	2	1	2	1	2	1	2	1	8	9	3	4	1	2	2	1	1	1	2	1	1	2	2	1	1	1	1	2	1	10	8	2	2	2	1	1	2	2	1	1	3	3	1	1	3	5	3	2	1	1	1	-4	1	-4	3	3	2	0	0	2	8	2	2	2	2	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	2	1	2	2	5	10	1	2	1	1	1	1	1	2	2	1	1	1	1	1	2	2	2	1	1	1	1	1	1	3	1	1	1	1	1	3	1	1	2	1	1	2	2	2	3	3	2	2	1	1	2	1	1	2	1	1	1	1	1	2	2	2	1	1	2	1	3	-4	-4	-4	-4	-4	1	-4	1	9	2	2	10	1	6	10	3	1	3	4	2	1	1	3	3	1	1	1	1	2	1	3	1	1	3	2	2	2	-1	2	2	-1	-4	-1	-1	2	4	8	2	-4	2	3	2	1	2	1	1	1	1	1	5	1	5	5	1	1	1	1	1	8	8	8	10	1	1	5	10	-4	1	1	2	1	2	9	3	9	2	2	1	1	1	1	1	1	2	-4	-4	-4	-4	-4	1	3	-4	3	2	-4	-4	-4	2	-4	-4	-4	-4	-4	-4	3	3	4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	1	-3	15602001	-4	-4	1	1962	28	10	-4	1	1	2	1	1	5	-4	2	1710	201	1	8	-4	156002	156001	-4	-4	-4	1	1	-4	-4	-4	1990	1562	1561990	3	2	2	0.361750	0.915	0.717338	0.915	0.0	0.33	0.33	0.220000	1.00	1.00	1.0	NaN	0.897000	0.66	0.0	0.0	0.0	0.000000	1.0	0.00	0.33	0.66	0.330000	1.00	1	1	1	1.000000	1	1.0	NaN	NaN	NaN	0.66	0.000000	0.777778	0.777778	0.518519	1.00	0.33	1.0	0.665	0.665	1.0	2	1560240001	1	1.5	15602211990	710
1	2	156	156	2	3	2	2	2	3	4	1	2	2	2	3	3	1	3	2	2	2	2	2	2	2	2	2	1	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	1	2	2	2	2	2	2	2	2	2	3	3	3	3	3	4	3	1	3	3	3	4	4	3	1	2	-4	1	-4	2	1	2	2	1	1	1	-4	-4	-4	1	1	1	1	2	1	1	2	1	2	2	1	6	3	3	4	1	1	2	2	2	1	1	1	1	1	1	1	2	1	1	2	2	6	6	2	1	2	2	1	2	2	1	4	2	1	1	1	3	4	2	2	1	1	1	-4	1	-4	2	3	2	0	0	2	8	2	2	2	2	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	2	2	2	2	5	3	6	3	-3	-3	-3	-3	-3	-3	-3	1	1	1	1	2	2	2	1	1	2	2	1	3	1	3	1	1	1	1	3	1	0	-3	1	1	1	2	2	1	1	1	1	1	1	2	2	1	1	1	1	1	2	2	2	2	2	2	1	1	1	1	2	-4	-4	-4	-4	-4	1	-4	2	8	2	2	8	2	5	5	1	4	2	4	1	3	1	1	1	1	1	3	1	1	2	-1	-1	2	-1	2	-1	3	-2	-1	-1	3	-4	2	2	-2	7	7	-2	-4	2	2	2	2	2	2	2	4	4	3	2	1	3	9	9	7	4	1	1	9	9	-1	10	8	3	2	8	-4	3	3	3	2	3	8	8	8	1	2	2	2	2	2	1	2	2	-4	-4	-4	-4	-4	2	2	-4	3	3	-4	-4	-4	3	-4	-4	-4	-4	-4	-4	2	2	3	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	1	-3	15602001	-4	-4	1	1963	27	10	-4	1	2	2	1	3	4	-4	2	2315	215	1	8	-4	156002	156001	-4	-4	-4	1	1	-4	-4	-4	1990	1562	1561990	3	3	1	0.853444	0.830	0.692741	0.915	1.0	0.66	0.33	0.663333	1.00	1.00	1.0	NaN	0.897000	0.66	1.0	1.0	1.0	1.000000	1.0	NaN	0.66	NaN	NaN	0.66	1	1	1	1.000000	1	0.0	NaN	NaN	NaN	0.66	0.000000	0.888889	0.888889	0.592593	1.00	1.00	0.0	0.500	0.500	1.0	2	1560240002	1	1.5	15602211990	710
2	2	156	156	3	2	1	3	2	3	4	1	4	3	2	2	3	3	3	3	2	2	1	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	1	2	-2	-2	-2	-2	-2	-2	-2	-2	-2	-2	-2	-2	-2	-2	1	2	-4	2	-4	2	1	2	2	1	1	1	-4	-4	-4	3	1	2	2	2	2	2	2	2	2	2	1	7	6	3	1	1	1	2	2	1	1	2	1	2	2	2	1	1	2	1	2	3	5	5	2	2	2	1	2	1	2	1	2	2	1	1	1	1	3	3	2	3	1	1	-4	1	-4	2	3	2	0	0	2	8	2	2	2	2	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	2	2	2	2	5	6	6	3	-3	-3	-3	-3	-3	-3	-3	1	1	2	2	2	2	2	1	1	2	3	1	3	2	3	1	2	1	2	2	1	0	-3	1	1	1	2	2	2	1	2	2	2	2	1	2	1	1	1	2	1	2	2	1	2	2	2	1	1	1	1	2	-4	-4	-4	-4	-4	1	-4	2	9	5	5	6	2	8	3	1	4	2	4	1	3	1	1	1	1	1	3	1	1	2	4	-1	2	3	3	-1	2	2	3	3	2	-4	-1	-1	2	7	7	-1	-4	1	2	2	2	3	2	4	3	5	5	2	1	5	8	6	7	4	4	3	8	-1	-1	8	3	4	4	9	-4	6	4	3	2	3	6	5	7	1	2	2	2	2	1	2	2	2	-4	-4	-4	-4	-4	2	2	-4	3	3	-4	-4	-4	3	-4	-4	-4	-4	-4	-4	2	2	3	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	1	-3	15602001	-4	-4	1	1964	26	10	-4	1	3	2	1	2	4	-4	2	2235	145	1	8	-4	156002	156001	-4	-4	-4	1	1	-4	-4	-4	1990	1562	1561990	3	3	2	0.767381	0.830	0.695135	0.830	1.0	0.66	0.33	0.663333	1.00	1.00	1.0	NaN	0.897000	0.66	1.0	1.0	1.0	1.000000	1.0	NaN	0.33	0.66	0.509190	0.66	1	1	1	1.000000	1	0.0	NaN	NaN	NaN	0.66	0.333333	0.777778	NaN	0.600444	0.66	1.00	0.0	0.500	0.500	1.0	2	1560240003	1	1.5	15602211990	710
3	2	156	156	4	1	2	2	1	4	3	2	1	3	2	2	2	3	1	3	2	2	2	2	1	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	1	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	4	4	3	5	3	1	1	2	4	3	5	1	2	2	-4	1	-4	1	2	2	2	1	1	1	-4	-4	-4	1	1	2	1	2	1	1	2	2	1	2	2	10	10	2	3	1	1	2	2	1	2	2	1	1	1	2	1	1	1	1	2	2	8	8	2	2	2	1	1	2	2	1	1	3	3	1	2	3	8	1	3	1	1	1	-4	2	-4	3	3	2	0	0	2	8	2	1	2	2	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	2	2	2	1	4	10	6	3	-3	-3	-3	-3	-3	-3	1	2	2	2	2	2	1	2	3	1	3	3	1	3	3	3	1	3	2	2	3	1	0	-3	0	1	2	2	3	1	2	2	2	1	1	2	3	1	1	2	1	2	1	2	1	2	2	2	1	1	1	1	3	-4	-4	-4	-4	-4	2	-4	1	10	1	1	9	1	8	8	1	2	1	4	1	-1	1	3	3	1	1	2	3	2	2	4	2	3	3	3	3	3	3	2	3	3	-4	2	2	3	4	1	-1	-4	1	1	2	1	1	3	1	1	1	1	1	1	1	1	4	1	4	1	1	-1	-1	7	10	7	1	2	10	-4	1	1	2	1	4	2	1	8	2	2	2	2	2	1	1	1	2	-4	-4	-4	-4	-4	1	1	-4	3	3	-4	-4	-4	3	-4	-4	-4	-4	-4	-4	3	3	4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	1	-3	15602001	-4	-4	1	1969	21	10	-4	6	9	2	1	1	6	-4	1	1920	205	1	8	-4	156002	156001	-4	-4	-4	1	1	-4	-4	-4	1990	1562	1561990	-2	2	2	0.556698	0.915	NaN	0.830	0.5	1.00	0.33	0.610000	1.00	0.66	1.0	NaN	0.733460	0.66	0.0	0.0	1.0	0.333333	1.0	0.33	0.66	0.66	0.550000	1.00	1	0	1	0.666667	1	1.0	NaN	NaN	NaN	0.66	0.000000	NaN	NaN	NaN	0.66	0.33	0.0	0.165	0.165	1.0	2	1560240004	1	1.5	15602211990	710
4	2	156	156	5	2	1	2	3	1	4	2	3	2	2	3	3	3	2	2	2	2	2	2	1	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	1	2	2	2	2	2	2	2	2	2	2	2	2	2	5	4	1	4	4	4	2	1	5	4	3	4	4	3	2	2	-4	1	-4	2	2	2	2	1	1	1	-4	-4	-4	1	1	2	1	2	1	2	1	2	1	2	1	9	10	4	3	1	1	1	1	1	1	1	1	1	2	2	1	1	1	1	2	1	10	9	2	1	2	1	1	2	2	1	2	3	1	1	1	3	10	3	4	1	1	1	-4	1	-4	3	3	2	0	0	2	8	2	2	2	3	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	2	1	2	2	5	10	1	2	1	1	1	1	1	2	2	1	1	1	1	2	2	2	2	1	3	2	1	3	2	1	1	2	1	2	1	1	2	2	3	1	1	2	3	1	2	2	2	2	1	1	1	1	1	1	1	2	2	1	2	2	2	2	1	1	1	1	1	-4	-4	-4	-4	-4	3	-4	2	7	8	6	6	4	2	9	2	1	1	2	1	3	1	1	3	1	2	3	1	2	1	4	1	1	1	2	2	1	1	1	3	1	-4	4	4	1	8	1	2	-4	1	2	1	2	2	1	2	1	1	1	1	1	1	3	1	1	1	1	1	4	1	1	3	1	1	1	8	-4	1	1	3	1	1	9	4	2	1	1	1	2	2	1	1	1	2	-4	-4	-4	-4	-4	1	2	-4	3	3	-4	-4	-4	3	-4	-4	-4	-4	-4	-4	2	3	3	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	1	-3	15602001	-4	-4	1	1947	43	10	-4	1	8	1	-3	-3	2	-4	3	1535	150	1	8	-4	156002	156001	-4	-4	-4	1	1	-4	-4	-4	1990	1562	1561990	2	2	1	0.335083	0.915	0.361412	0.915	1.0	0.00	0.33	0.443333	1.00	1.00	1.0	NaN	0.897000	0.66	0.0	0.0	0.0	0.000000	1.0	0.00	0.00	0.00	0.000000	1.00	1	0	1	0.666667	1	0.0	NaN	NaN	NaN	0.66	0.000000	0.333333	0.000000	0.111111	1.00	0.33	0.0	0.165	0.165	1.0	2	1560240005	1	1.5	15602211990	710
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995	2	156	156	996	2	2	2	2	2	2	2	2	3	2	2	2	2	2	2	2	1	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	1	1	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	4	4	1	5	5	1	1	5	5	5	2	5	4	1	2	2	-4	2	-4	2	2	2	2	1	1	1	-4	-4	-4	1	1	2	1	2	1	1	2	2	1	1	2	9	7	4	2	2	1	2	2	2	2	2	1	2	2	1	1	2	1	1	2	2	10	5	1	2	2	1	1	2	2	1	1	2	1	3	1	3	10	3	3	1	1	1	-4	3	-4	1	3	1	49	0	1	6	2	1	1	1	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	2	10	1	1	4	10	1	2	1	1	2	2	2	2	2	1	1	1	1	2	2	2	1	1	1	3	1	1	3	3	1	3	1	2	1	3	6	6	4	1	1	1	1	2	2	2	2	3	2	1	1	2	2	1	1	2	1	1	2	1	2	2	1	2	1	2	3	-4	-4	-4	-4	-4	3	-4	3	10	5	3	1	1	5	5	4	2	1	3	2	4	1	-1	3	1	3	-1	2	1	1	1	1	1	1	1	-1	3	1	-1	3	3	-4	-1	-1	-1	6	1	2	-4	1	3	2	4	1	2	1	1	1	1	1	1	1	7	1	1	1	1	1	6	1	1	6	1	1	1	10	-4	1	1	3	2	1	10	5	3	1	2	2	2	2	2	1	1	2	-4	-4	-4	-4	-4	1	2	-4	-1	-1	-4	-4	-4	-1	-4	-4	-4	-4	-4	-4	-1	2	-1	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	1	-3	15602003	-4	-4	1	1948	42	5	-4	1	7	1	-3	-3	2	-4	4	1039	150	3	6	-4	156004	156041	-4	-4	-4	1	1	-4	-4	-4	1990	1562	1561990	1	1	-1	0.213500	0.915	0.248148	0.915	NaN	0.00	0.33	0.246220	0.66	0.33	0.0	0.833333	0.387778	1.00	0.0	0.0	0.0	0.000000	1.0	0.00	0.66	0.00	0.220000	1.00	0	0	1	0.333333	1	0.0	NaN	NaN	NaN	0.66	0.000000	0.555556	0.000000	0.185185	1.00	0.00	0.0	0.000	0.000	1.0	2	1560240996	1	1.5	15602211990	710
996	2	156	156	997	1	1	2	3	2	4	1	2	1	1	3	4	3	4	1	2	2	2	2	1	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	1	2	2	2	2	2	2	2	2	2	2	2	2	2	3	3	4	5	3	5	1	1	4	5	3	5	5	3	1	2	-4	1	-4	2	2	1	2	2	2	2	-4	-4	-4	1	1	2	1	2	1	2	1	2	1	2	1	10	10	2	4	1	1	1	1	1	1	1	1	1	2	2	1	1	1	1	2	1	9	8	2	2	1	2	2	2	2	1	4	3	3	1	1	3	9	2	4	1	2	1	-4	1	-4	3	3	1	49	0	1	8	2	1	1	3	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	3	1	2	2	5	10	1	2	1	1	1	1	2	2	2	2	1	1	1	2	2	2	3	1	3	3	1	2	3	2	1	3	2	3	1	2	2	2	2	1	1	2	3	2	1	1	2	2	2	1	1	2	1	1	1	2	1	2	1	2	2	2	1	1	1	1	1	-4	-4	-4	-4	-4	3	-4	2	8	9	5	3	1	3	9	1	4	2	1	1	3	1	1	3	1	2	2	1	2	1	4	2	1	2	2	3	2	1	2	3	1	-4	3	4	1	5	8	2	-4	1	1	1	2	2	2	1	1	1	1	1	1	1	1	1	1	1	1	1	9	9	1	8	1	1	1	10	-4	1	1	3	2	1	7	9	10	1	2	2	1	1	2	1	2	2	-4	-4	-4	-4	-4	1	3	-4	4	4	-4	-4	-4	4	-4	-4	-4	-4	-4	-4	3	3	4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	1	-3	15602003	-4	-4	2	1953	37	10	-4	1	3	2	1	3	6	-4	2	1631	230	2	5	-4	156004	156041	-4	-4	-4	1	1	-4	-4	-4	1990	1562	1561990	2	2	2	0.375917	0.915	0.555454	0.915	0.5	0.00	0.33	0.276667	1.00	1.00	1.0	NaN	0.897000	0.66	0.0	0.0	0.0	0.000000	1.0	0.33	0.33	0.33	0.330000	1.00	1	0	1	0.666667	1	1.0	NaN	NaN	NaN	0.66	0.000000	0.888889	0.888889	0.592593	1.00	0.66	0.0	0.330	0.330	1.0	2	1560240997	1	1.5	15602211990	710
997	2	156	156	998	1	1	2	3	1	2	1	2	2	2	4	3	2	2	2	2	2	2	2	1	2	2	2	2	2	1	2	2	2	2	2	2	2	2	2	2	2	1	2	2	2	2	2	1	2	2	2	2	2	2	2	5	5	5	5	5	2	2	2	5	4	5	5	5	5	2	2	-4	2	-4	2	2	2	1	1	1	2	-4	-4	-4	3	1	2	2	2	1	2	2	2	2	2	2	8	8	3	4	1	2	2	2	1	1	1	2	2	2	2	2	2	2	2	2	3	5	6	2	2	1	1	2	2	2	1	1	2	1	2	1	1	5	1	3	3	2	1	-4	1	-4	3	3	2	0	0	2	8	2	1	1	2	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	2	6	2	2	4	8	1	2	2	1	1	2	2	2	2	2	1	1	2	2	2	2	2	1	1	2	2	3	1	3	1	3	1	1	1	1	1	1	2	1	1	1	3	1	2	1	1	1	1	2	2	2	1	1	2	1	1	2	1	2	2	2	1	1	1	1	1	-4	-4	-4	-4	-4	1	-4	1	9	1	2	5	1	1	8	1	4	2	4	1	3	1	2	3	1	1	3	1	2	1	2	1	3	4	3	3	4	4	3	3	4	-4	3	3	3	8	4	1	-4	2	2	2	1	2	1	6	3	3	5	3	1	1	8	9	9	1	1	2	9	10	8	9	6	6	1	10	-4	1	1	3	4	3	5	8	9	1	2	2	1	2	2	2	2	2	-4	-4	-4	-4	-4	1	2	-4	3	3	-4	-4	-4	2	-4	-4	-4	-4	-4	-4	3	3	4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	1	-3	15602001	-4	-4	1	1962	28	10	-4	1	1	1	-3	-3	2	-4	4	2135	140	3	6	-4	156002	156041	-4	-4	-4	1	1	-4	-4	-4	1990	1562	1561990	3	3	2	0.642849	0.915	0.704037	0.915	1.0	0.66	0.33	0.663333	1.00	0.33	1.0	NaN	0.574730	0.66	1.0	1.0	0.0	0.666667	1.0	0.00	1.00	1.00	0.666667	1.00	1	1	1	1.000000	1	0.0	NaN	NaN	NaN	0.66	0.000000	0.888889	1.000000	0.629630	1.00	1.00	0.0	0.500	0.500	1.0	2	1560240998	1	1.5	15602211990	710
998	2	156	156	999	1	1	2	2	3	4	2	3	2	2	2	3	3	3	3	2	2	2	2	1	2	2	2	1	1	1	1	2	2	2	2	2	2	1	2	2	2	1	2	2	2	1	1	1	1	2	2	2	2	2	2	4	5	5	5	4	5	3	1	5	5	4	4	3	1	1	2	-4	1	-4	2	2	2	2	1	1	1	-4	-4	-4	3	1	2	2	2	1	1	2	2	1	2	1	6	8	3	4	1	2	2	2	2	2	1	2	2	2	2	2	2	2	1	2	3	7	4	2	2	1	2	1	2	2	1	2	1	3	2	2	3	8	1	2	1	1	1	-4	1	-4	3	3	2	0	0	2	8	2	2	2	3	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	4	1	2	2	5	9	1	2	1	1	2	1	2	2	2	2	1	2	2	2	2	2	3	1	3	2	1	2	3	2	1	3	1	3	1	1	1	1	2	1	2	2	3	1	2	3	2	1	2	1	1	2	1	1	1	1	1	2	2	2	2	2	1	1	1	1	3	-4	-4	-4	-4	-4	2	-4	2	8	6	5	4	5	9	7	1	4	3	1	1	3	1	2	3	1	1	1	1	2	1	4	2	2	2	3	3	2	2	3	2	2	-4	3	3	2	8	2	2	-4	2	1	3	2	1	2	1	1	1	1	1	1	1	6	1	1	1	1	1	5	3	1	8	1	1	1	10	-4	1	1	1	2	2	4	2	8	1	2	2	2	2	2	2	2	2	-4	-4	-4	-4	-4	1	3	-4	3	3	-4	-4	-4	3	-4	-4	-4	-4	-4	-4	3	3	3	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	1	-3	15602001	-4	-4	2	1962	28	8	-4	1	4	2	1	5	3	-4	3	1930	130	2	6	-4	156002	156041	-4	-4	-4	1	1	-4	-4	-4	1990	1562	1561990	1	1	1	0.361750	0.915	0.436778	0.915	0.0	0.33	0.33	0.220000	1.00	1.00	1.0	NaN	0.897000	0.66	0.0	0.0	0.0	0.000000	1.0	0.33	0.33	0.33	0.330000	1.00	1	1	1	1.000000	1	1.0	NaN	NaN	NaN	0.66	0.000000	0.444444	0.222222	0.222222	1.00	0.00	0.0	0.000	0.000	1.0	2	1560240999	1	1.5	15602211990	710
999	2	156	156	1000	1	1	1	3	4	4	2	2	2	2	3	3	2	2	2	2	2	2	2	1	2	2	2	1	2	2	2	2	2	2	2	2	2	2	2	2	2	1	1	2	2	2	2	2	2	2	2	2	2	2	2	5	4	2	4	4	4	1	1	4	3	4	4	3	1	2	2	-4	2	-4	1	2	2	2	2	2	2	-4	-4	-4	2	1	2	1	2	1	2	1	2	1	2	1	8	8	4	3	1	1	2	2	2	1	2	1	1	2	2	1	2	2	1	2	1	8	7	2	1	2	2	2	2	2	1	2	2	1	2	2	3	7	2	3	1	1	1	-4	1	-4	3	3	2	0	0	2	8	2	2	2	2	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-1	1	2	2	5	7	1	2	2	1	2	1	2	2	2	2	1	2	2	2	2	2	2	2	2	3	1	3	2	3	1	1	1	2	2	1	1	1	2	1	1	2	2	3	2	3	2	1	2	1	2	2	1	2	1	2	1	2	1	2	1	2	1	1	1	1	2	-4	-4	-4	-4	-4	1	-4	2	9	3	3	6	2	3	3	1	4	2	1	1	2	1	3	3	1	1	-1	1	1	1	4	2	1	2	2	3	3	3	3	2	1	-4	2	2	2	4	5	2	-4	1	1	1	1	2	1	1	1	1	1	1	1	1	1	2	1	1	1	1	7	6	6	10	1	6	6	10	-4	1	1	1	2	3	6	3	7	1	1	2	1	2	1	1	1	2	-4	-4	-4	-4	-4	2	2	-4	2	3	-4	-4	-4	2	-4	-4	-4	-4	-4	-4	3	3	4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	-4	1	-3	15602001	-4	-4	1	1946	44	10	-4	1	3	1	-3	-3	6	-4	2	1900	128	2	8	-4	156002	156041	-4	-4	-4	1	1	-4	-4	-4	1990	1562	1561990	2	2	2	0.459330	0.830	0.498973	0.915	NaN	0.66	0.33	0.500320	0.66	1.00	1.0	NaN	0.897000	0.66	0.0	0.0	0.0	0.000000	1.0	0.33	0.66	0.33	0.440000	1.00	1	0	1	0.666667	1	0.0	NaN	NaN	NaN	0.66	0.000000	0.666667	0.555556	0.407408	1.00	0.66	0.0	0.330	0.330	1.0	2	1560241000	1	1.5	15602211990	710

1000 rows × 477 columns

Isolate the columns that are related to family, children, and parents.

While I wish there was an easier way to do this, I had to isolate each column manually, not with the handy filter function...

fam_df1990 = df1990[['V4: Important in life: Work', 
                    'V5: Important in life: Family', 
                    'V6: Important in life: Friends',
                    'V132: Satisfaction with financial situation of household',  
                    'V225: Parents responsibilities to their children', 
                    'V224: Respect and love for parents', 
                    'V236: Important child qualities: obedience']]
fam_df1990 = fam_df1990.rename(columns={"V4: Important in life: Work": "Important in life: Work", 
                                        "V5: Important in life: Family": "Important in life: Family", 
                                        "V132: Satisfaction with financial situation of household": "Satisfaction with financial situation of household", 
                                        "V236: Important child qualities: obedience":"Important child qualities: obedience",
                                        "V6: Important in life: Friends":"Important in life: Friends"})
fam_df1990

	Important in life: Work	Important in life: Family	Important in life: Friends	Satisfaction with financial situation of household	V225: Parents responsibilities to their children	V224: Respect and love for parents	Important child qualities: obedience
0	1	2	2	5	2	1	2
1	3	2	2	4	2	2	2
2	2	1	3	3	2	1	2
3	1	2	2	8	3	2	2
4	2	1	2	10	1	1	2
...	...	...	...	...	...	...	...
995	2	2	2	10	1	1	2
996	1	1	2	9	1	1	2
997	1	1	2	5	2	2	2
998	1	1	2	8	1	1	2
999	1	1	1	7	2	1	2

1000 rows × 7 columns

fam_df1990['Year'] = 1990
columns = ['Important in life: Work', 'Important in life: Family', 'Important child qualities: obedience', 'Important in life: Friends']
fam1990 = fam_df1990.groupby('Year')[columns].mean()
fam1990.reset_index()

	Year	Important in life: Work	Important in life: Family	Important child qualities: obedience	Important in life: Friends
0	1990	1.463	1.423	1.915	2.041

1995

fam_df1995 = df1995[['V4: Important in life: Family',
                     'V5: Important in life: Friends',
                     'V8: Important in life: Work', 
                     'V12: Respect and love for parents', 
                     'V13: Parents responsibilities to their children',
                     'V64: Satisfaction with financial situation of household',
                     'V70: One of main goals in life has been to make my parents proud',
                     'V24: Important child qualities: obedience']]
fam_df1995 = fam_df1995.rename(columns={"V8: Important in life: Work": "Important in life: Work", 
                                        "V4: Important in life: Family": "Important in life: Family", 
                                        "V64: Satisfaction with financial situation of household": "Satisfaction with financial situation of household", 
                                        "V24: Important child qualities: obedience":"Important child qualities: obedience",
                                        "V5: Important in life: Friends":"Important in life: Friends", 
                                        "V70: One of main goals in life has been to make my parents proud":"Make parents proud"})
fam_df1995 = fam_df1995.reset_index()
fam_df1995['Year'] = 1995
columns2 = ['Important in life: Work', 'Important in life: Family', 'Important child qualities: obedience', 'Important in life: Friends', 'Make parents proud']
fam1995 = fam_df1995.groupby('Year')[columns2].mean()
fam1995.reset_index()

	Year	Important in life: Work	Important in life: Family	Important child qualities: obedience	Important in life: Friends	Make parents proud
0	1995	1.372667	1.240667	1.707333	1.797333	1.910667

2001

fam_df2001 = df2001[['V4: Important in life: Family', 
                     'V5: Important in life: Friends', 
                     'V8: Important in life: Work', 
                     'V13: Respect and love for parents', 
                     'V14: Parents responsibilities to their children', 
                     'V80: Satisfaction with financial situation of household', 
                     'V24: Important child qualities: obedience',
                     'V113: One of main goals in life has been to make my parents proud']]
fam_df2001 = fam_df2001.rename(columns={"V8: Important in life: Work": "Important in life: Work", 
                                        "V4: Important in life: Family": "Important in life: Family", 
                                        "V80: Satisfaction with financial situation of household": "Satisfaction with financial situation of household", 
                                        "V24: Important child qualities: obedience":"Important child qualities: obedience",
                                        "V5: Important in life: Friends":"Important in life: Friends",
                                        "V113: One of main goals in life has been to make my parents proud":"Make parents proud"})
fam_df2001 = fam_df2001.reset_index()
fam_df2001['Year'] = 2001
fam2001 = fam_df2001.groupby('Year')[columns2].mean()
fam2001.reset_index()

	Year	Important in life: Work	Important in life: Family	Important child qualities: obedience	Important in life: Friends	Make parents proud
0	2001	1.536	1.403	1.852	1.892	1.835

2007

fam_df2007 = df2007[['V4: Important in life: Family', 
                     'V5: Important in life: Friends',
                     'V8: Important in life: Work',
                     'V21: Important child qualities: obedience', 
                     'V68: Satisfaction with financial situation of household',
                     'V64: One of main goals in life has been to make my parents proud']]
fam_df2007 = fam_df2007.rename(columns={"V8: Important in life: Work": "Important in life: Work", 
                                        "V4: Important in life: Family": "Important in life: Family", 
                                        "V68: Satisfaction with financial situation of household": "Satisfaction with financial situation of household", 
                                        "V21: Important child qualities: obedience":"Important child qualities: obedience",
                                        "V5: Important in life: Friends":"Important in life: Friends", 
                                        "V64: One of main goals in life has been to make my parents proud":"Make parents proud"})
fam_df2007 = fam_df2007.reset_index()
fam_df2007['Year'] = 2007
fam2007 = fam_df2007.groupby('Year')[columns2].mean()
fam2007.reset_index()

	Year	Important in life: Work	Important in life: Family	Important child qualities: obedience	Important in life: Friends	Make parents proud
0	2007	1.500251	1.201406	1.852838	1.744852	1.489201

2012

fam_df2012 = df2012[['V4: Important in life: Family', 
                     'V5: Important in life: Friends',
                     'V8: Important in life: Work',
                     'V21: Important child qualities: obedience', 
                     'V59: Satisfaction with financial situation of household', 
                     'V49: One of main goals in life has been to make my parents proud']]
fam_df2012 = fam_df2012.rename(columns={"V8: Important in life: Work": "Important in life: Work", 
                                        "V4: Important in life: Family": "Important in life: Family", 
                                        "V59: Satisfaction with financial situation of household": "Satisfaction with financial situation of household", 
                                        "V21: Important child qualities: obedience":"Important child qualities: obedience",
                                        "V5: Important in life: Friends":"Important in life: Friends", 
                                        "V49: One of main goals in life has been to make my parents proud":"Make parents proud"})
fam_df2012 = fam_df2012.reset_index()
fam_df2012['Year'] = 2012
fam2012 = fam_df2012.groupby('Year')[columns2].mean()
fam2012.reset_index()

	Year	Important in life: Work	Important in life: Family	Important child qualities: obedience	Important in life: Friends	Make parents proud
0	2012	1.656522	1.105652	1.927391	1.530435	1.800435

2018

fam_df2018 = df2018[['Q1: Important in life: Family', 
                     'Q2: Important in life: Friends', 
                     'Q5: Important in life: Work', 
                     'Q17: Important child qualities: obedience', 
                     'Q50: Satisfaction with financial situation of household', 
                     'Q27: One of main goals in life has been to make my parents proud']]
fam_df2018 = fam_df2018.rename(columns={"Q5: Important in life: Work": "Important in life: Work", 
                                        "Q1: Important in life: Family": "Important in life: Family", 
                                        "Q50: Satisfaction with financial situation of household": "Satisfaction with financial situation of household", 
                                        "Q17: Important child qualities: obedience":"Important child qualities: obedience",
                                        "Q2: Important in life: Friends":"Important in life: Friends", 
                                        "Q27: One of main goals in life has been to make my parents proud":"Make parents proud"})
fam_df2018 = fam_df2018.reset_index()
fam_df2018['Year'] = 2018
fam2018 = fam_df2018.groupby('Year')[columns2].mean()
fam2018.reset_index()

	Year	Important in life: Work	Important in life: Family	Important child qualities: obedience	Important in life: Friends	Make parents proud
0	2018	1.699934	1.137022	1.925889	1.721014	2.026021

Concat the Mean Data...

dfall = pd.concat([fam1990, fam1995, fam2001, fam2007, fam2012, fam2018])
dfall = dfall.reset_index()

dfall

	Year	Important in life: Work	Important in life: Family	Important child qualities: obedience	Important in life: Friends	Make parents proud
0	1990	1.463000	1.423000	1.915000	2.041000	NaN
1	1995	1.372667	1.240667	1.707333	1.797333	1.910667
2	2001	1.536000	1.403000	1.852000	1.892000	1.835000
3	2007	1.500251	1.201406	1.852838	1.744852	1.489201
4	2012	1.656522	1.105652	1.927391	1.530435	1.800435
5	2018	1.699934	1.137022	1.925889	1.721014	2.026021

Melt the Data

dfall_long = pd.melt(dfall, id_vars='Year')
dfall_long.set_index(['variable', 'Year'])
#dfall_long = dfall_long[dfall_long['variable_1'] == 'mean']
#dfall_long = dfall_long.dropna()
#dfall_long = dfall_long.sort_values(by='Year').sort_index()
#dfall_long = dfall_long.groupby('variable_0')

		value
variable	Year
Important in life: Work	1990	1.463000
	1995	1.372667
	2001	1.536000
	2007	1.500251
	2012	1.656522
	2018	1.699934
Important in life: Family	1990	1.423000
	1995	1.240667
	2001	1.403000
	2007	1.201406
	2012	1.105652
	2018	1.137022
Important child qualities: obedience	1990	1.915000
	1995	1.707333
	2001	1.852000
	2007	1.852838
	2012	1.927391
	2018	1.925889
Important in life: Friends	1990	2.041000
	1995	1.797333
	2001	1.892000
	2007	1.744852
	2012	1.530435
	2018	1.721014
Make parents proud	1990	NaN
	1995	1.910667
	2001	1.835000
	2007	1.489201
	2012	1.800435
	2018	2.026021

China's Economic Growth

gdp = pd.read_csv('/Users/kelsey.schoeman/Box Sync/Forge - Node/World Values Survey/GDP All.csv')
gdp = gdp[gdp['Country Code'] == 'CHN']
gdp = gdp.drop(columns=['Country Name', 'Country Code', 'Indicator Name', 'Indicator Code'])
gdp = gdp.melt()
gdp = gdp.rename(columns={"variable":'Year'})
gdp = gdp[gdp['Year'] >= '1995'].dropna()
gdp['Year'] = pd.to_datetime(gdp['Year'])
gdp = px.line(gdp, x='Year', y='value', 
              title='The Economic Growth of the Peoples Republic of China', 
              labels={'value':'GDP ($)'})
gdp
#gdp.update_layout(xaxis = dict(tickmode = 'linear', dtick = 10)

Line Chart - Family Values

dfall_long = px.line(dfall_long, x='Year', y='value', color='variable', range_y=[0, 3], 
                     title='Family Values in China (1990-2018)',
                     labels={'value':'Value', 'variable':'Survey Question'},
                     hover_name='variable')
dfall_long.update_traces(mode='markers+lines')
#Very Important = 3, Rather Important = 2, Not Very Important = 1